# US 2021/0241731 A1 · Systems, devices, and methods for assigning mood labels to musical compositions > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0241731 A1 - Document ID: US20210241731A1 - Status: Published application - Kind: A1 - Application number: 17/163,282 - Filed: 2021-01-29 - Published: 2021-08-05 - Earliest claimed: 2020-01-31 - Assignees: Obeebo Labs Ltd. - Applicants: Obeebo Labs Ltd. - Inventors: Colin P. Williams - Web page: https://patentgrep.com/patent/US20210241731A1 ## CPC - G10H 1/0025: {Automatic or semi-automatic music composition, e.g. producing random music, applying rules from music theory or modifying a musical piece (automatically producing a series of tones G10H1/26)} - G06F 16/65: Clustering; Classification - G06Q 40/12: Accounting - G06Q 50/184: {Intellectual property management} - G10H 1/0008: {Associated control or indicating means} - G10H 1/0041: {in coded form} - G10H 7/08: by calculating functions or polynomial approximations to evaluate amplitudes at successive sample points of a tone waveform - G10H 2210/031: Musical analysis, i.e. isolation, extraction or identification of musical elements or musical parameters from a raw acoustic signal or from an encoded audio signal - G10H 2210/056: for extraction or identification of individual instrumental parts, e.g. melody, chords, bass; Identification or separation of instrumental parts by their characteristic voices or timbres - G10H 2210/061: for extraction of musical phrases, isolation of musically relevant segments, e.g. musical thumbnail generation, or for temporal structure analysis of a musical piece, e.g. determination of the movement sequence of a musical work - G10H 2210/071: for rhythm pattern analysis or rhythm style recognition - G10H 2210/076: for extraction of timing, tempo; Beat detection - G10H 2210/111: Automatic composing, i.e. using predefined musical rules - G10H 2210/131: Morphing, i.e. transformation of a musical piece into a new different one, e.g. remix - G10H 2210/145: Composing rules, e.g. harmonic or musical rules, for use in automatic composition; Rule generation algorithms therefor - G10H 2240/085: Mood, i.e. generation, detection or selection of a particular emotional content or atmosphere in a musical piece - G10H 2250/005: Algorithms for electrophonic musical instruments or musical processing, e.g. for automatic composition or resource allocation - G10H 2250/215: Transforms, i.e. mathematical transforms into domains appropriate for musical signal processing, coding or compression - G10H 2250/311: Neural networks for electrophonic musical instruments or musical processing, e.g. for musical recognition or control, automatic composition or improvisation - G10H: ELECTROPHONIC MUSICAL INSTRUMENTS; INSTRUMENTS IN WHICH THE TONES ARE GENERATED BY ELECTROMECHANICAL MEANS OR ELECTRONIC GENERATORS, OR IN WHICH THE TONES ARE SYNTHESISED FROM A DATA STORE - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract Computer-based systems, devices, and methods for assigning mood labels to musical compositions are described. A mood classifier is trained based on mood-labeled musically-coherent segments of musical compositions and subsequently applied to automatically assign mood labels to musically-coherent segments of musical compositions. In both cases, the musically-coherent segments are generated using automated segmentation algorithms. ## Claims ### Claim 1 A computer-implemented method of labeling a musical composition with at least one mood label, the method comprising: segmenting the musical composition into a sequence of musically-coherent segments, the sequence of musically-coherent segments including at least two musically-coherent segments; and employing a mood classifier to assign at least one respective mood label to each respective musically-coherent segment. ### Claim 2 (depends on 1) The method of claim 1 wherein the mood classifier comprises processor-executable instructions and/or data stored in a non-transitory processor-readable storage medium that, when executed by at least one processor, cause the at least one processor to assign at least one respective mood label to each respective musically-coherent segment of a musical composition, and wherein employing a mood classifier to assign at least one respective mood label to each respective musically-coherent segment includes executing, by at least one processor, the processor-executable instructions and/or data stored in a non-transitory processor-readable storage medium communicatively coupled to the at least one processor to cause the at least one processor to assign at least one respective mood label to each respective musically-coherent segment. ### Claim 3 (depends on 1) The method of claim 1 wherein the mood classifier comprises processor-executable instructions and/or data stored in a non-transitory processor-readable storage medium, wherein the processor-executable instructions and/or data are based at least in part on training data comprising a library of mood-labeled musically-coherent segments. ### Claim 4 (depends on 1) The method of claim 1, further comprising: labelling the musical composition with a least one mood label, the at least one mood label with which the musical composition is labelled corresponding to at least one mood label assigned to at least one musically-coherent segment of the musical composition. ### Claim 5 (depends on 4) The method of claim 4 wherein labeling the musical composition with at least one mood label includes labeling the musical composition with all distinct mood labels assigned to all of the musically-coherent segments of the musical composition. ### Claim 6 (depends on 4) The method of claim 4 wherein labeling the musical composition with at least one mood label includes labeling the musical composition with a subset of all distinct mood labels assigned to all of the musically-coherent segments of the musical composition, the subset consisting of only X most frequently occurring distinct mood labels among all distinct mood labels assigned to all of the musically-coherent segments of the musical composition, where X is an integer and X≥1. ### Claim 7 (depends on 4) The method of claim 4 wherein labeling the musical composition with at least one mood label includes combining at least two of the respective mood labels assigned to respective ones of at least two of the musically-coherent segments into at least one subsuming mood label and labelling the musical composition with the at least one subsuming mood label. ### Claim 8 (depends on 1) The method of claim 1 wherein: segmenting the musical composition into a sequence of musically-coherent segments includes segmenting the musical composition into at least one musically-coherent segment selected from a group consisting of: an intro, a verse, a chorus, a solo, a middle8, a pre-chorus, a bridge, and an outro; and employing a mood classifier to assign at least one respective mood label to each respective musically-coherent segment includes employing the mood classifier to assign, to at least one musically-coherent segment, at least one mood label selected from a group consisting of: Energetic, Heroic, Happy, Relaxed, Calm, Dreamy, Awed, Romantic, Sensual, Sexual, Wistful, Sad, Melancholic, Depressed, Anxious, Fearful, Angry, Hateful, Restless, Whimsical, Amusing, Annoying, Tense, Beautiful, Relaxing, Serene, Energizing, Erotic, Desirous, Indignant, Defiant, Joyful, Cheerful, Depressing, Scary, Fearful, and Triumphant. ### Claim 9 A mood classifier comprising; processor-executable instructions and/or data stored in a non-transitory processor-readable storage medium, wherein when executed by at least one processor communicatively coupled to the non-transitory processor-readable storage medium, the mood classifier assigns at least one mood label to at least a portion of a musical composition, and wherein the processor-executable instructions and/or data are based at least in part on training data comprising a library of mood-labeled musically-coherent segments. ### Claim 10 (depends on 9) The mood classifier of claim 9 wherein the musical composition comprises a sequence of musically-coherent segments, and wherein when executed by at least one processor communicatively coupled to the non-transitory processor-readable storage medium, the mood classifier assigns at least one respective mood label to each musically-coherent segment in the musical composition. ### Claim 11 (depends on 9) The mood classifier of claim 9 wherein the library of mood-labeled musically-coherent segments is generated from a library of musical compositions by a process that, for each musical composition in the library of musical compositions: segments the musical composition into musically-coherent segments; and assigns at least one mood label to each musically-coherent segment. ### Claim 12 (depends on 11) The mood classifier of claim 11 wherein the library of mood-labeled musically-coherent segments is generated from a library of musical compositions by a process that, for each musical composition in the library of musical compositions: segments the musical composition into musically-coherent segments; and assigns only one respective mood label to each musically-coherent segment. ### Claim 13 (depends on 9) The mood classifier of claim 9 wherein the library of musically-coherent segments includes at least one musically-coherent segment selected from a group consisting of: an intro, a verse, a chorus, a solo, a middle8, a pre-chorus, a bridge, and an outro. ### Claim 14 (depends on 9) The mood classifier of claim 9 wherein the at least one mood label is selected from a group consisting of: Energetic, Heroic, Happy, Relaxed, Calm, Dreamy, Awed, Romantic, Sensual, Sexual, Wistful, Sad, Melancholic, Depressed, Anxious, Fearful, Angry, Hateful, Restless, Whimsical, Amusing, Annoying, Tense, Beautiful, Relaxing, Serene, Energizing, Erotic, Desirous, Indignant, Defiant, Joyful, Cheerful, Depressing, Scary, Fearful, and Triumphant. ### Claim 15 A computer-implemented method of training a mood classifier to assign at least one mood label to a musical composition, the method comprising: generating mood-labeled musically-coherent segments, wherein generating mood-labeled musically-coherent segments includes, for a set of musical compositions: segmenting each musical composition into a sequence of musically-coherent segments; and assigning at least one mood label to each musically-coherent segment; and training the mood classifier based on the mood-labeled musically-coherent segments. ### Claim 16 (depends on 15) The method of claim 15 wherein assigning at least one mood label to each musically-coherent segment includes receiving, by at least one processor, an assignment of at least one mood label for at least one musically-coherent segment. ### Claim 17 (depends on 15) The method of claim 15 wherein assigning at least one mood label to each musically-coherent segment includes assigning only one respective mood label to each musically-coherent segment. ### Claim 18 (depends on 15) The method of claim 15 wherein: segmenting each musical composition into a sequence of musically-coherent segments includes segmenting each musical composition into at least one musically-coherent segment selected from a group consisting of: an intro, a verse, a chorus, a solo, a middle8, a pre-chorus, a bridge, and an outro; and assigning at least one mood label to each musically-coherent segment includes assigning, to each musically-coherent segment, at least one mood label selected from a group consisting of: Energetic, Heroic, Happy, Relaxed, Calm, Dreamy, Awed, Romantic, Sensual, Sexual, Wistful, Sad, Melancholic, Depressed, Anxious, Fearful, Angry, Hateful, Restless, Whimsical, Amusing, Annoying, Tense, Beautiful, Relaxing, Serene, Energizing, Erotic, Desirous, Indignant, Defiant, Joyful, Cheerful, Depressing, Scary, Fearful, and Triumphant. ### Claim 19 (depends on 15) The method of claim 15 wherein each mood-labeled musically-coherent segment comprises a respective set of digital data objects, and wherein training the mood classifier based on the mood-labeled musically-coherent segments includes extracting training features from parameters specified in the digital data objects. ### Claim 20 (depends on 15) The method of claim 15 wherein training the mood classifier based on the mood-labeled musically-coherent segments includes, for each mood-labeled musically-coherent segment, constructing a respective audio waveform and extracting training features from properties of the audio waveform.